




from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowerCAmelCase_						 =					logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		_A   (    _lowerCamelCase ):






  def __init__(    self						:      Tuple ,		_A						:      Any ,		_A						:      Dict )						->       Optional[Any]:






       """simple docstring"""







       super().__init__()
       self.register_modules(unet=_A ,		scheduler=_A )






  @torch.no_grad()
  def __call__(    self						:      Dict ,		_A						:      Tuple = 1 ,		_A						:      int = 100 ,		_A						:      int = None ,		_A						:      List[str] = None ,		_A						:      Tuple = True ,		)						->       Union[AudioPipelineOutput, Tuple]:






       """simple docstring"""







       if audio_length_in_s is None:
            lowercase   :  Any        =					self.unet.config.sample_size / self.unet.config.sample_rate

       lowercase   :  List[str]        =					audio_length_in_s * self.unet.config.sample_rate

       lowercase   :  List[Any]        =					2 ** len(self.unet.up_blocks )
       if sample_size < 3 * down_scale_factor:
            raise ValueError(
                f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

       lowercase   :  List[Any]        =					int(_A )
       if sample_size % down_scale_factor != 0:
            lowercase   :  Union[str, Any]        =					(
                (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
            ) * down_scale_factor
            logger.info(
                f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                ''' process.''' )
       lowercase   :  Any        =					int(_A )

       lowercase   :  Union[str, Any]        =					next(iter(self.unet.parameters() ) ).dtype
       lowercase   :  Dict        =					(batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(_A ,		_A ) and len(_A ) != batch_size:
            raise ValueError(
                f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

       lowercase   :  Dict        =					randn_tensor(_A ,		generator=_A ,		device=self.device ,		dtype=_A )

       # set step values
       self.scheduler.set_timesteps(_A ,		device=audio.device )
       lowercase   :  List[Any]        =					self.scheduler.timesteps.to(_A )

       for t in self.progress_bar(self.scheduler.timesteps ):
            # 1. predict noise model_output
            lowercase   :  Any        =					self.unet(_A ,		_A ).sample

            # 2. compute previous image: x_t -> t_t-1
            lowercase   :  List[Any]        =					self.scheduler.step(_A ,		_A ,		_A ).prev_sample

       lowercase   :  Optional[Any]        =					audio.clamp(-1 ,		1 ).float().cpu().numpy()

       lowercase   :  Optional[Any]        =					audio[:, :, :original_sample_size]

       if not return_dict:
            return (audio,)

       return AudioPipelineOutput(audios=_A )





'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



